58 citations · 186 across the 30 of their papers we have counts for
17 papers · 1 filter
Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendations
Mihaela Rotar, Theresia Veronika Rampisela, Maria Maistro
Large Language Models (LLMs) can infer sensitive attributes such as gender or age from indirect cues like names and pronouns, potentially biasing recommendations. While several deb…
Post-Training Denoising of User Profiles with LLMs in Collaborative Filtering Recommendation
Ervin Dervishaj, Maria Maistro, Tuukka Ruotsalo +1
Implicit feedback -- the main data source for training Recommender Systems (RSs) -- is inherently noisy and has been shown to negatively affect recommendation effectiveness. Denois…
Stairway to Fairness: Connecting Group and Individual Fairness
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +2
Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relation…
Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier
Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro +1
Fairness and relevance are two important aspects of recommender systems (RSs). Typically, they are evaluated either (i) separately by individual measures of fairness and relevance,…
Are Representation Disentanglement and Interpretability Linked in Recommendation Models? A Critical Review and Reproducibility Study
Ervin Dervishaj, Tuukka Ruotsalo, Maria Maistro +1
Unsupervised learning of disentangled representations has been closely tied to enhancing the representation intepretability of Recommender Systems (RSs). This has been achieved by…
Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro +1
Relevance and fairness are two major objectives of recommender systems (RSs). Recent work proposes measures of RS fairness that are either independent from relevance (fairness-only…
Dataset and Models for Item Recommendation Using Multi-Modal User Interactions
Simone Borg Bruun, Krisztian Balog, Maria Maistro
While recommender systems with multi-modal item representations (image, audio, and text), have been widely explored, learning recommendations from multi-modal user interactions (e.…
Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain
Simone Borg Bruun, Christina Lioma, Maria Maistro
Providing personalized recommendations for insurance products is particularly challenging due to the intrinsic and distinctive features of the insurance domain. First, unlike more…
Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we e…
Graph-based Recommendation for Sparse and Heterogeneous User Interactions
Simone Borg Bruun, Kacper Kenji Lesniak, Mirko Biasini +4
Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses strug…
Principled Multi-Aspect Evaluation Measures of Rankings
Maria Maistro, Lucas Chaves Lima, Jakob Grue Simonsen +1
Information Retrieval evaluation has traditionally focused on defining principled ways of assessing the relevance of a ranked list of documents with respect to a query. Several met…
Learning Recommendations from User Actions in the Item-poor Insurance Domain
Simone Borg Bruun, Maria Maistro, Christina Lioma
While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations i…
Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks
Tetsuya Sakai, Sijie Tao, Maria Maistro +7
Unfortunately, the official English (sub)task results reported in the NTCIR-14 WWW-2, NTCIR-15 WWW-3, and NTCIR-16 WWW-4 overview papers are incorrect due to noise in the official…
repro_eval: A Python Interface to Reproducibility Measures of System-oriented IR Experiments
Timo Breuer, Nicola Ferro, Maria Maistro +1
In this work we introduce repro_eval - a tool for reactive reproducibility studies of system-oriented information retrieval (IR) experiments. The corresponding Python package provi…
University of Copenhagen Participation in TREC Health Misinformation Track 2020
Lucas Chaves Lima, Dustin Brandon Wright, Isabelle Augenstein +1
In this paper, we describe our participation in the TREC Health Misinformation Track 2020. We submitted runs to the Total Recall Task and 13 runs to the Ad Hoc task. Our appro…
Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19
Lucas Chaves Lima, Casper Hansen, Christian Hansen +5
This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the…
How to Measure the Reproducibility of System-oriented IR Experiments
Timo Breuer, Nicola Ferro, Norbert Fuhr +4
Replicability and reproducibility of experimental results are primary concerns in all the areas of science and IR is not an exception. Besides the problem of moving the field towar…